Clinical diagnosis systems aided by artificial intelligence are well known in the art. Although theoretical foundation and practical application of such diagnosis systems have evolved through many years, there is still a lack of acceptance by clinicians leading to a rare usage of such systems.
A particular reason for this lacking acceptance is due to the fact that known diagnosis systems are designed with the aim of delivering an entire diagnosis which is able to explain all symptoms.
This holistic approach, however, often leads to a resulting diagnosis based on detailed and vast disease-sets which is far away from meeting the requirements of clinicians.
Instead of delivering a specific multi-fault diagnosis based on multiple symptoms it would be more beneficial to deliver a single-fault diagnosis. However, a single disease itself cannot entirely “explain” all symptoms.